Overview

Dataset statistics

Number of variables16
Number of observations3216
Missing cells14444
Missing cells (%)28.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory427.1 KiB
Average record size in memory136.0 B

Variable types

Numeric12
Categorical4

Alerts

lo_id is highly overall correlated with week_first_login_this_year and 1 other fieldsHigh correlation
first_attempt_avg_score is highly overall correlated with final_score_avg and 1 other fieldsHigh correlation
train_count_avg is highly overall correlated with final_score_avg and 1 other fieldsHigh correlation
final_score_avg is highly overall correlated with first_attempt_avg_score and 1 other fieldsHigh correlation
train_count_80_avg is highly overall correlated with first_attempt_avg_score and 1 other fieldsHigh correlation
oz_done_count is highly overall correlated with oz_subject_count and 1 other fieldsHigh correlation
oz_subject_count is highly overall correlated with oz_done_countHigh correlation
oz_percent_avg is highly overall correlated with oz_done_countHigh correlation
course_start is highly overall correlated with course_50High correlation
course_50 is highly overall correlated with course_startHigh correlation
week_first_login_this_year is highly overall correlated with lo_idHigh correlation
wave_login_first is highly overall correlated with lo_idHigh correlation
recomended is highly imbalanced (62.0%)Imbalance
olymp_start is highly imbalanced (70.6%)Imbalance
first_attempt_avg_score has 306 (9.5%) missing valuesMissing
train_count_avg has 175 (5.4%) missing valuesMissing
final_score_avg has 306 (9.5%) missing valuesMissing
train_count_80_avg has 1306 (40.6%) missing valuesMissing
oz_done_count has 1612 (50.1%) missing valuesMissing
oz_subject_count has 1906 (59.3%) missing valuesMissing
oz_percent_avg has 1612 (50.1%) missing valuesMissing
course_start has 2194 (68.2%) missing valuesMissing
course_50 has 2490 (77.4%) missing valuesMissing
olymp_start has 2537 (78.9%) missing valuesMissing
lo_id has unique valuesUnique
train_count_avg has 799 (24.8%) zerosZeros
train_count_80_avg has 317 (9.9%) zerosZeros
oz_done_count has 294 (9.1%) zerosZeros
oz_percent_avg has 294 (9.1%) zerosZeros
week_first_login_this_year has 211 (6.6%) zerosZeros

Reproduction

Analysis started2023-07-21 06:24:04.743818
Analysis finished2023-07-21 06:24:21.069034
Duration16.33 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

lo_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean584832.78
Minimum381823
Maximum657785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:21.162517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum381823
5-th percentile463357
Q1544430.25
median607955.5
Q3632336.25
95-th percentile654523.5
Maximum657785
Range275962
Interquartile range (IQR)87906

Descriptive statistics

Standard deviation60630.477
Coefficient of variation (CV)0.10367148
Kurtosis0.42090572
Mean584832.78
Median Absolute Deviation (MAD)39222.5
Skewness-0.95797347
Sum1.8808222 × 109
Variance3.6760547 × 109
MonotonicityNot monotonic
2023-07-21T10:24:21.287494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
538975 1
 
< 0.1%
614071 1
 
< 0.1%
614492 1
 
< 0.1%
614476 1
 
< 0.1%
614448 1
 
< 0.1%
614406 1
 
< 0.1%
614399 1
 
< 0.1%
614295 1
 
< 0.1%
614191 1
 
< 0.1%
614180 1
 
< 0.1%
Other values (3206) 3206
99.7%
ValueCountFrequency (%)
381823 1
< 0.1%
382951 1
< 0.1%
383221 1
< 0.1%
383966 1
< 0.1%
384839 1
< 0.1%
386193 1
< 0.1%
387007 1
< 0.1%
387357 1
< 0.1%
387739 1
< 0.1%
387748 1
< 0.1%
ValueCountFrequency (%)
657785 1
< 0.1%
657636 1
< 0.1%
657603 1
< 0.1%
657374 1
< 0.1%
657352 1
< 0.1%
657332 1
< 0.1%
657314 1
< 0.1%
657281 1
< 0.1%
657277 1
< 0.1%
657276 1
< 0.1%

recomended
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.2 KiB
0
2979 
1
 
237

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2979
92.6%
1 237
 
7.4%

Length

2023-07-21T10:24:21.397505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T10:24:21.504716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2979
92.6%
1 237
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2979
92.6%
1 237
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2979
92.6%
1 237
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2979
92.6%
1 237
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2979
92.6%
1 237
 
7.4%

first_attempt_avg_score
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1572
Distinct (%)54.0%
Missing306
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean50.827859
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:21.603488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.0909
Q134.0925
median53.062167
Q368.760925
95-th percentile86.392767
Maximum100
Range100
Interquartile range (IQR)34.668425

Descriptive statistics

Standard deviation23.07853
Coefficient of variation (CV)0.45405277
Kurtosis-0.8154916
Mean50.827859
Median Absolute Deviation (MAD)17.2842
Skewness-0.21479799
Sum147909.07
Variance532.61854
MonotonicityNot monotonic
2023-07-21T10:24:21.729020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.0909 78
 
2.4%
18.1818 48
 
1.5%
50 32
 
1.0%
27.2727 28
 
0.9%
72.7273 25
 
0.8%
54.5455 24
 
0.7%
14.2857 21
 
0.7%
2.9412 20
 
0.6%
28.5714 18
 
0.6%
45.4545 18
 
0.6%
Other values (1562) 2598
80.8%
(Missing) 306
 
9.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
2.9412 20
0.6%
3.125 9
0.3%
3.8462 1
 
< 0.1%
5.04205 2
 
0.1%
5.8824 16
0.5%
6.01605 3
 
0.1%
6.10795 2
 
0.1%
6.25 8
 
0.2%
6.39166667 1
 
< 0.1%
ValueCountFrequency (%)
100 7
0.2%
98.71793333 1
 
< 0.1%
97.61903333 1
 
< 0.1%
96.15385 1
 
< 0.1%
96.15383333 1
 
< 0.1%
95.83333333 1
 
< 0.1%
95.68763333 1
 
< 0.1%
95.45455 2
 
0.1%
94.8718 1
 
< 0.1%
94.79166667 1
 
< 0.1%

train_count_avg
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct130
Distinct (%)4.3%
Missing175
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean3.535075
Minimum0
Maximum248.6667
Zeros799
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:21.850015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile13
Maximum248.6667
Range248.6667
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.6631859
Coefficient of variation (CV)2.4506371
Kurtosis248.50464
Mean3.535075
Median Absolute Deviation (MAD)1
Skewness11.799111
Sum10750.163
Variance75.05079
MonotonicityNot monotonic
2023-07-21T10:24:21.974304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 799
24.8%
1 267
 
8.3%
0.3333 204
 
6.3%
0.6667 146
 
4.5%
0.5 114
 
3.5%
2 113
 
3.5%
1.3333 113
 
3.5%
1.6667 104
 
3.2%
2.3333 99
 
3.1%
3 88
 
2.7%
Other values (120) 994
30.9%
(Missing) 175
 
5.4%
ValueCountFrequency (%)
0 799
24.8%
0.3333 204
 
6.3%
0.5 114
 
3.5%
0.6667 146
 
4.5%
1 267
 
8.3%
1.3333 113
 
3.5%
1.5 22
 
0.7%
1.6667 104
 
3.2%
2 113
 
3.5%
2.3333 99
 
3.1%
ValueCountFrequency (%)
248.6667 1
< 0.1%
131 1
< 0.1%
91.3333 1
< 0.1%
90.6667 1
< 0.1%
89.6667 1
< 0.1%
89 1
< 0.1%
74.3333 1
< 0.1%
73.3333 1
< 0.1%
72.6667 1
< 0.1%
66.3333 1
< 0.1%

final_score_avg
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct934
Distinct (%)32.1%
Missing306
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean67.734217
Minimum2.9412
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:22.109097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.9412
5-th percentile9.0909
Q144.904254
median81.6288
Q392.803033
95-th percentile100
Maximum100
Range97.0588
Interquartile range (IQR)47.898779

Descriptive statistics

Standard deviation29.852973
Coefficient of variation (CV)0.44073697
Kurtosis-0.86085046
Mean67.734217
Median Absolute Deviation (MAD)18.094425
Skewness-0.69997935
Sum197106.57
Variance891.20001
MonotonicityNot monotonic
2023-07-21T10:24:22.259434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 336
 
10.4%
9.0909 76
 
2.4%
95.83333333 61
 
1.9%
18.1818 46
 
1.4%
81.6288 39
 
1.2%
84.6591 37
 
1.2%
96.9697 34
 
1.1%
92.80303333 34
 
1.1%
87.6894 31
 
1.0%
90.9091 28
 
0.9%
Other values (924) 2188
68.0%
(Missing) 306
 
9.5%
ValueCountFrequency (%)
2.9412 20
0.6%
3.125 9
0.3%
3.8462 1
 
< 0.1%
5.04205 2
 
0.1%
5.8824 15
0.5%
6.01605 2
 
0.1%
6.10795 2
 
0.1%
6.25 8
 
0.2%
6.39166667 1
 
< 0.1%
6.51265 1
 
< 0.1%
ValueCountFrequency (%)
100 336
10.4%
99.0196 4
 
0.1%
98.95833333 11
 
0.3%
98.71793333 15
 
0.5%
98.0392 3
 
0.1%
97.91666667 9
 
0.3%
97.61903333 4
 
0.1%
97.4359 8
 
0.2%
97.05883333 4
 
0.1%
97.0588 1
 
< 0.1%

train_count_80_avg
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct28
Distinct (%)1.5%
Missing1306
Missing (%)40.6%
Infinite0
Infinite (%)0.0%
Mean1.3196325
Minimum0
Maximum8.3333
Zeros317
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:22.373470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3333
median1
Q32
95-th percentile3.6667
Maximum8.3333
Range8.3333
Interquartile range (IQR)1.6667

Descriptive statistics

Standard deviation1.1654517
Coefficient of variation (CV)0.88316385
Kurtosis2.6305144
Mean1.3196325
Median Absolute Deviation (MAD)0.6667
Skewness1.3467783
Sum2520.4981
Variance1.3582778
MonotonicityNot monotonic
2023-07-21T10:24:22.474997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 365
 
11.3%
0 317
 
9.9%
0.3333 177
 
5.5%
0.6667 170
 
5.3%
2 170
 
5.3%
1.3333 154
 
4.8%
1.6667 144
 
4.5%
2.3333 77
 
2.4%
3 71
 
2.2%
2.6667 54
 
1.7%
Other values (18) 211
 
6.6%
(Missing) 1306
40.6%
ValueCountFrequency (%)
0 317
9.9%
0.3333 177
5.5%
0.5 36
 
1.1%
0.6667 170
5.3%
1 365
11.3%
1.3333 154
4.8%
1.5 26
 
0.8%
1.6667 144
 
4.5%
2 170
5.3%
2.3333 77
 
2.4%
ValueCountFrequency (%)
8.3333 1
 
< 0.1%
7 1
 
< 0.1%
6.6667 4
0.1%
6.3333 1
 
< 0.1%
6 3
0.1%
5.6667 3
0.1%
5.3333 7
0.2%
5 6
0.2%
4.6667 7
0.2%
4.5 1
 
< 0.1%

oz_done_count
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct76
Distinct (%)4.7%
Missing1612
Missing (%)50.1%
Infinite0
Infinite (%)0.0%
Mean10.001247
Minimum0
Maximum237
Zeros294
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:22.585518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q313
95-th percentile38.85
Maximum237
Range237
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.476302
Coefficient of variation (CV)1.4474497
Kurtosis42.452572
Mean10.001247
Median Absolute Deviation (MAD)5
Skewness4.2862596
Sum16042
Variance209.56332
MonotonicityNot monotonic
2023-07-21T10:24:22.703136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 294
 
9.1%
1 168
 
5.2%
2 131
 
4.1%
3 88
 
2.7%
4 79
 
2.5%
5 73
 
2.3%
6 69
 
2.1%
8 66
 
2.1%
7 64
 
2.0%
9 46
 
1.4%
Other values (66) 526
 
16.4%
(Missing) 1612
50.1%
ValueCountFrequency (%)
0 294
9.1%
1 168
5.2%
2 131
4.1%
3 88
 
2.7%
4 79
 
2.5%
5 73
 
2.3%
6 69
 
2.1%
7 64
 
2.0%
8 66
 
2.1%
9 46
 
1.4%
ValueCountFrequency (%)
237 1
< 0.1%
101 1
< 0.1%
92 1
< 0.1%
88 1
< 0.1%
87 1
< 0.1%
83 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%
77 1
< 0.1%
71 1
< 0.1%

oz_subject_count
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)0.9%
Missing1906
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean1.7816794
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:22.804674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum15
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2427207
Coefficient of variation (CV)0.69749961
Kurtosis21.620413
Mean1.7816794
Median Absolute Deviation (MAD)0
Skewness3.3398497
Sum2334
Variance1.5443547
MonotonicityNot monotonic
2023-07-21T10:24:22.885699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 737
 
22.9%
2 309
 
9.6%
3 170
 
5.3%
4 55
 
1.7%
5 20
 
0.6%
6 7
 
0.2%
7 6
 
0.2%
8 2
 
0.1%
15 1
 
< 0.1%
11 1
 
< 0.1%
Other values (2) 2
 
0.1%
(Missing) 1906
59.3%
ValueCountFrequency (%)
1 737
22.9%
2 309
9.6%
3 170
 
5.3%
4 55
 
1.7%
5 20
 
0.6%
6 7
 
0.2%
7 6
 
0.2%
8 2
 
0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
0.1%
7 6
 
0.2%
6 7
 
0.2%
5 20
 
0.6%
4 55
 
1.7%
3 170
5.3%

oz_percent_avg
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct500
Distinct (%)31.2%
Missing1612
Missing (%)50.1%
Infinite0
Infinite (%)0.0%
Mean42.332368
Minimum0
Maximum100
Zeros294
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:23.000603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119.230769
median43.669872
Q363.528139
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)44.297369

Descriptive statistics

Standard deviation29.462951
Coefficient of variation (CV)0.6959911
Kurtosis-0.89637004
Mean42.332368
Median Absolute Deviation (MAD)21.821121
Skewness0.11608917
Sum67901.118
Variance868.06551
MonotonicityNot monotonic
2023-07-21T10:24:23.125136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 294
 
9.1%
50 110
 
3.4%
100 83
 
2.6%
33.33333333 47
 
1.5%
25 38
 
1.2%
60 36
 
1.1%
66.66666667 36
 
1.1%
40 34
 
1.1%
75 28
 
0.9%
80 22
 
0.7%
Other values (490) 876
27.2%
(Missing) 1612
50.1%
ValueCountFrequency (%)
0 294
9.1%
2.08333333 1
 
< 0.1%
2.7027027 1
 
< 0.1%
4.34782609 1
 
< 0.1%
5.71428571 1
 
< 0.1%
6 1
 
< 0.1%
6.52173913 1
 
< 0.1%
6.74603174 1
 
< 0.1%
6.97674419 1
 
< 0.1%
7.14285714 1
 
< 0.1%
ValueCountFrequency (%)
100 83
2.6%
96.875 1
 
< 0.1%
96 1
 
< 0.1%
95.45454545 2
 
0.1%
93.75 1
 
< 0.1%
93.33333333 2
 
0.1%
92.85714286 1
 
< 0.1%
92.67676768 1
 
< 0.1%
92.30769231 2
 
0.1%
92.10526316 1
 
< 0.1%

course_start
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)1.4%
Missing2194
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean2.2093933
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:23.225664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile7
Maximum18
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0681792
Coefficient of variation (CV)0.93608465
Kurtosis7.6621561
Mean2.2093933
Median Absolute Deviation (MAD)0
Skewness2.4839254
Sum2258
Variance4.2773652
MonotonicityNot monotonic
2023-07-21T10:24:23.313483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 579
 
18.0%
2 173
 
5.4%
3 105
 
3.3%
4 51
 
1.6%
5 27
 
0.8%
6 27
 
0.8%
7 20
 
0.6%
8 16
 
0.5%
9 9
 
0.3%
10 8
 
0.2%
Other values (4) 7
 
0.2%
(Missing) 2194
68.2%
ValueCountFrequency (%)
1 579
18.0%
2 173
 
5.4%
3 105
 
3.3%
4 51
 
1.6%
5 27
 
0.8%
6 27
 
0.8%
7 20
 
0.6%
8 16
 
0.5%
9 9
 
0.3%
10 8
 
0.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
13 1
 
< 0.1%
12 2
 
0.1%
11 3
 
0.1%
10 8
 
0.2%
9 9
 
0.3%
8 16
0.5%
7 20
0.6%
6 27
0.8%
5 27
0.8%

course_50
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)1.5%
Missing2490
Missing (%)77.4%
Infinite0
Infinite (%)0.0%
Mean1.5606061
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:23.417065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3405266
Coefficient of variation (CV)0.85897819
Kurtosis17.024019
Mean1.5606061
Median Absolute Deviation (MAD)0
Skewness3.6562347
Sum1133
Variance1.7970115
MonotonicityNot monotonic
2023-07-21T10:24:23.536706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 541
 
16.8%
2 96
 
3.0%
3 41
 
1.3%
4 19
 
0.6%
6 13
 
0.4%
7 8
 
0.2%
8 3
 
0.1%
5 2
 
0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
(Missing) 2490
77.4%
ValueCountFrequency (%)
1 541
16.8%
2 96
 
3.0%
3 41
 
1.3%
4 19
 
0.6%
5 2
 
0.1%
6 13
 
0.4%
7 8
 
0.2%
8 3
 
0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
8 3
 
0.1%
7 8
 
0.2%
6 13
 
0.4%
5 2
 
0.1%
4 19
 
0.6%
3 41
1.3%
2 96
3.0%

olymp_start
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)0.4%
Missing2537
Missing (%)78.9%
Memory size50.2 KiB
1.0
617 
2.0
 
59
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2037
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 617
 
19.2%
2.0 59
 
1.8%
3.0 3
 
0.1%
(Missing) 2537
78.9%

Length

2023-07-21T10:24:23.643551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T10:24:23.752890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 617
90.9%
2.0 59
 
8.7%
3.0 3
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 679
33.3%
0 679
33.3%
1 617
30.3%
2 59
 
2.9%
3 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1358
66.7%
Other Punctuation 679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 679
50.0%
1 617
45.4%
2 59
 
4.3%
3 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 679
33.3%
0 679
33.3%
1 617
30.3%
2 59
 
2.9%
3 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 679
33.3%
0 679
33.3%
1 617
30.3%
2 59
 
2.9%
3 3
 
0.1%

week_first_login_this_year
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6072761
Minimum0
Maximum26
Zeros211
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:23.842643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q316
95-th percentile23
Maximum26
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.5561761
Coefficient of variation (CV)0.78650556
Kurtosis-1.1189847
Mean9.6072761
Median Absolute Deviation (MAD)6
Skewness0.4598294
Sum30897
Variance57.095798
MonotonicityNot monotonic
2023-07-21T10:24:23.948632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 283
 
8.8%
2 276
 
8.6%
3 229
 
7.1%
0 211
 
6.6%
9 181
 
5.6%
21 181
 
5.6%
10 176
 
5.5%
5 149
 
4.6%
7 135
 
4.2%
20 132
 
4.1%
Other values (17) 1263
39.3%
ValueCountFrequency (%)
0 211
6.6%
1 283
8.8%
2 276
8.6%
3 229
7.1%
4 102
 
3.2%
5 149
4.6%
6 120
3.7%
7 135
4.2%
8 123
3.8%
9 181
5.6%
ValueCountFrequency (%)
26 5
 
0.2%
25 4
 
0.1%
24 63
 
2.0%
23 101
3.1%
22 102
3.2%
21 181
5.6%
20 132
4.1%
19 98
3.0%
18 42
 
1.3%
17 34
 
1.1%

wave_login_first
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.2 KiB
0.0
1709 
1.0
831 
2.0
473 
3.0
203 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9648
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1709
53.1%
1.0 831
25.8%
2.0 473
 
14.7%
3.0 203
 
6.3%

Length

2023-07-21T10:24:24.049304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T10:24:24.158288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1709
53.1%
1.0 831
25.8%
2.0 473
 
14.7%
3.0 203
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 4925
51.0%
. 3216
33.3%
1 831
 
8.6%
2 473
 
4.9%
3 203
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6432
66.7%
Other Punctuation 3216
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4925
76.6%
1 831
 
12.9%
2 473
 
7.4%
3 203
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 3216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9648
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4925
51.0%
. 3216
33.3%
1 831
 
8.6%
2 473
 
4.9%
3 203
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4925
51.0%
. 3216
33.3%
1 831
 
8.6%
2 473
 
4.9%
3 203
 
2.1%

week_first_login_wave
Real number (ℝ)

Distinct53
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.757774
Minimum0
Maximum52
Zeros27
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size50.2 KiB
2023-07-21T10:24:24.269032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median14
Q322
95-th percentile41
Maximum52
Range52
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.447144
Coefficient of variation (CV)0.68309457
Kurtosis0.45384134
Mean16.757774
Median Absolute Deviation (MAD)7
Skewness0.92665595
Sum53893
Variance131.03711
MonotonicityNot monotonic
2023-07-21T10:24:24.391118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 195
 
6.1%
9 179
 
5.6%
10 176
 
5.5%
22 138
 
4.3%
20 135
 
4.2%
7 122
 
3.8%
13 121
 
3.8%
14 120
 
3.7%
23 119
 
3.7%
3 115
 
3.6%
Other values (43) 1796
55.8%
ValueCountFrequency (%)
0 27
 
0.8%
1 72
2.2%
2 93
2.9%
3 115
3.6%
4 87
2.7%
5 105
3.3%
6 100
3.1%
7 122
3.8%
8 103
3.2%
9 179
5.6%
ValueCountFrequency (%)
52 14
0.4%
51 12
0.4%
50 10
0.3%
49 9
0.3%
48 2
 
0.1%
47 11
0.3%
46 8
0.2%
45 16
0.5%
44 15
0.5%
43 9
0.3%

grade
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.2 KiB
8.0
1110 
6.0
1064 
7.0
1042 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9648
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row7.0
5th row7.0

Common Values

ValueCountFrequency (%)
8.0 1110
34.5%
6.0 1064
33.1%
7.0 1042
32.4%

Length

2023-07-21T10:24:24.499902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-21T10:24:24.604536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8.0 1110
34.5%
6.0 1064
33.1%
7.0 1042
32.4%

Most occurring characters

ValueCountFrequency (%)
. 3216
33.3%
0 3216
33.3%
8 1110
 
11.5%
6 1064
 
11.0%
7 1042
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6432
66.7%
Other Punctuation 3216
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3216
50.0%
8 1110
 
17.3%
6 1064
 
16.5%
7 1042
 
16.2%
Other Punctuation
ValueCountFrequency (%)
. 3216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9648
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3216
33.3%
0 3216
33.3%
8 1110
 
11.5%
6 1064
 
11.0%
7 1042
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3216
33.3%
0 3216
33.3%
8 1110
 
11.5%
6 1064
 
11.0%
7 1042
 
10.8%

Interactions

2023-07-21T10:24:18.911772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:05.751357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.928202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.191897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.341345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.504016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:11.796320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.911872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.083138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:15.240488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.630818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.768407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.010624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:05.858757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:07.026585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.291080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.437988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.598541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:11.894939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.009767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.181124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:15.540681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.726229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.864559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.108552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:05.954366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:07.120994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.387275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.530654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.689946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:11.986604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.105839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.277562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:15.641158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.822399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.960729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.206428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.049720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:07.217500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.479339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.625127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.782940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.080683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.202340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.371590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:15.742522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.922387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:18.056144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.307642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.145003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:07.313689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.574660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.719201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.884149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.171136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.298963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.462857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:15.848209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.020495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:18.150450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.397842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.241044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:07.403113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.666550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.814478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.971656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.263547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.398253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.554777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:15.955004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.118880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:18.241778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.487324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.333811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:07.494116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.758761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.904170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:11.216351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.351451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.492178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.647113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.046632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.204039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:18.334507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.585396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.432415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:07.592765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.857595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.004689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:11.315935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.449455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.590942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.755440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.145727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.296898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:18.433909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.682452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.526761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:07.801921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.951917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.097606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:11.407063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.542509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.691877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.851608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.241413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.387869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:18.528530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.787468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.631793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:07.906087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.057482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.205182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:11.511463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.637979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.789280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:14.952164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.340505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.485170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:18.628623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.889122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.734829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.001575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.150339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.299667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:11.605325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.725533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.880633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:15.045683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.436409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.578578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:18.721913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:19.991667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:06.831325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:08.096097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:09.245487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:10.397574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:11.698425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:12.820451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:13.981579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:15.143499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:16.535738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:17.671894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-21T10:24:18.817096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-21T10:24:24.699676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
lo_idfirst_attempt_avg_scoretrain_count_avgfinal_score_avgtrain_count_80_avgoz_done_countoz_subject_countoz_percent_avgcourse_startcourse_50week_first_login_this_yearweek_first_login_waverecomendedolymp_startwave_login_firstgrade
lo_id1.000-0.084-0.150-0.138-0.059-0.101-0.062-0.100-0.219-0.2010.583-0.0270.0930.0590.7290.097
first_attempt_avg_score-0.0841.0000.3090.742-0.7020.2610.1360.2710.1610.187-0.1030.0190.2030.0000.0680.209
train_count_avg-0.1500.3091.0000.7500.5910.2620.2360.0990.2730.219-0.312-0.0490.0730.0000.0330.079
final_score_avg-0.1380.7420.7501.000-0.0160.3430.2400.2210.2890.263-0.257-0.0150.2070.1400.0640.255
train_count_80_avg-0.059-0.7020.591-0.0161.000-0.096-0.010-0.238-0.013-0.054-0.159-0.0790.0850.1450.0000.123
oz_done_count-0.1010.2610.2620.343-0.0961.0000.5720.6020.2030.234-0.149-0.0170.1760.0000.0500.099
oz_subject_count-0.0620.1360.2360.240-0.0100.5721.000-0.0910.2660.279-0.187-0.0460.1110.0000.0000.121
oz_percent_avg-0.1000.2710.0990.221-0.2380.602-0.0911.0000.0340.069-0.0710.0010.1360.0780.0570.153
course_start-0.2190.1610.2730.289-0.0130.2030.2660.0341.0000.710-0.377-0.0110.1580.1350.0960.158
course_50-0.2010.1870.2190.263-0.0540.2340.2790.0690.7101.000-0.3390.0240.1880.1550.0910.113
week_first_login_this_year0.583-0.103-0.312-0.257-0.159-0.149-0.187-0.071-0.377-0.3391.0000.2140.1480.0000.2730.039
week_first_login_wave-0.0270.019-0.049-0.015-0.079-0.017-0.0460.001-0.0110.0240.2141.0000.1200.0950.2970.075
recomended0.0930.2030.0730.2070.0850.1760.1110.1360.1580.1880.1480.1201.0000.0790.0890.048
olymp_start0.0590.0000.0000.1400.1450.0000.0000.0780.1350.1550.0000.0950.0791.0000.0410.144
wave_login_first0.7290.0680.0330.0640.0000.0500.0000.0570.0960.0910.2730.2970.0890.0411.0000.097
grade0.0970.2090.0790.2550.1230.0990.1210.1530.1580.1130.0390.0750.0480.1440.0971.000

Missing values

2023-07-21T10:24:20.150646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-21T10:24:20.438247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-21T10:24:20.927940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

lo_idrecomendedfirst_attempt_avg_scoretrain_count_avgfinal_score_avgtrain_count_80_avgoz_done_countoz_subject_countoz_percent_avgcourse_startcourse_50olymp_startweek_first_login_this_yearwave_login_firstweek_first_login_wavegrade
0538975077.9412000.666786.7647000.0000NaNNaNNaN4.0NaN1.00.01.015.08.0
1499683042.5388331.666755.7423000.00003.01.033.3333331.0NaN1.00.03.038.08.0
3500082053.8833673.000083.2951332.33331.01.050.0000003.01.0NaN0.03.040.08.0
4505971151.9886675.0000100.0000002.333329.01.061.7021281.0NaNNaN1.02.011.07.0
5574074073.4848677.6667100.0000000.666759.02.086.7647062.01.01.01.01.012.07.0
6577350063.8403332.000083.3624671.000012.04.030.7692315.0NaNNaN0.01.014.06.0
7436886187.20863339.0000100.0000000.000088.05.063.6861316.03.01.02.02.040.06.0
9495168157.1429000.000057.142900NaNNaNNaNNaN1.01.01.02.03.030.08.0
10569985035.60606725.000090.8144004.00004.02.026.7857142.01.02.00.01.05.07.0
11599374025.0573003.666738.069800NaN0.0NaN0.0000001.01.01.00.01.038.08.0
lo_idrecomendedfirst_attempt_avg_scoretrain_count_avgfinal_score_avgtrain_count_80_avgoz_done_countoz_subject_countoz_percent_avgcourse_startcourse_50olymp_startweek_first_login_this_yearwave_login_firstweek_first_login_wavegrade
4578625936065.0568330.666765.0568330.0000NaNNaNNaNNaNNaNNaN9.00.09.07.0
4580625735043.7500000.000043.750000NaNNaNNaNNaNNaNNaNNaN10.00.010.07.0
4582625695013.6363500.000013.636350NaNNaNNaNNaNNaNNaNNaN9.00.09.07.0
4584625598024.2897500.500024.289750NaNNaNNaNNaNNaNNaN2.010.00.010.07.0
4585625546027.2727000.500027.272700NaNNaNNaNNaN1.0NaNNaN9.00.09.07.0
4586625507015.6250000.000015.625000NaNNaNNaNNaNNaNNaNNaN9.00.09.07.0
4587625473027.2727000.000027.272700NaN13.03.061.904762NaNNaN1.010.00.010.07.0
4589625282018.7500000.000018.750000NaNNaNNaNNaNNaNNaNNaN9.00.09.07.0
4591625150057.95456714.0000100.0000001.66675.01.045.4545452.01.0NaN9.00.09.07.0
4592625146031.2500000.000031.250000NaNNaNNaNNaNNaNNaNNaN9.00.09.07.0